---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
- Pytorch
- Text Classification
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-finetuned-emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9235
    - name: F1
      type: f1
      value: 0.923296474937779
language:
- en
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Distilbert-finetuned-emotion

Distilbert is a variant of bert model(one of LLM models). This model with a classification head is used to classify the emotions of the input tweet. 
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2195
- Accuracy: 0.9235
- F1: 0.9233

## Emotion Labels

- **label_0:** Sadness
- **label_1:** Joy
- **label_2:** Love
- **label_3:** Anger
- **label_4:** Fear
- **label_5:** Surprise

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8537        | 1.0   | 250  | 0.3235          | 0.897    | 0.8958 |
| 0.2506        | 2.0   | 500  | 0.2195          | 0.9235   | 0.9233 |

### Validation metrics

- test_loss : 0.2194512039422989
- test_accuracy : 0.9235
- test_f1 : 0.923296474937779


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1